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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Elida Madona Siburian
Politeknik Bisnis Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Evaluasi Leakage-Aware dan Imbalance-Sensitive pada BiLSTM dan Machine Learning Klasik untuk Klasifikasi Arah Pergerakan Harga Emas ANTAM Juni Ismail; Randi Sumitro; Juliana Rotua Pasaribu; Elida Madona Siburian; Renovand Mikael Situmorang
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1346

Abstract

ANTAM gold is a widely used hedging instrument among Indonesian investors, yet determining the right moment to transact remains difficult because of its volatile and non-linear price movements. Several prior studies have reported near-perfect predictive accuracy; however, such results frequently stem from evaluation procedures that are prone to data leakage and therefore do not reflect genuine generalization ability. This study develops a leakage-aware and imbalance-sensitive evaluation framework for classifying the directional movement of ANTAM gold prices. Daily price data from 2010 to 2025 (5,751 samples) are transformed into 14 technical features—comprising lagged log-returns, volatility, momentum, moving-average ratios, and RSI—and labelled according to the sign of the five-day forward return. A Bidirectional Long Short-Term Memory (BiLSTM) model is benchmarked against Random Forest, Decision Tree, and a majority-class baseline using five-fold walk-forward validation with purging and train-only feature scaling. Performance is assessed through Balanced Accuracy, Macro-F1, the Matthews Correlation Coefficient (MCC), ROC-AUC, and PR-AUC. All classifiers outperform the majority baseline, with Decision Tree attaining the highest Macro-F1 of 0.534, followed by Random Forest (0.510) and BiLSTM (0.497), and a best MCC of 0.074. These findings indicate limited but real directional predictability and confirm that rigorous evaluation yields markedly more conservative and credible performance estimates than the inflated accuracies claimed in earlier work.
Analisis Komparatif YOLO11 dan RT-DETR untuk Deteksi Sampah pada Variasi Pencahayaan Juni Ismail; Pangidoan Adventus Ambarita; Renovand Mikael Situmorang; Elida Madona Siburian; Inggrid Ester Erlinda Simarmata
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1358

Abstract

Sistem pemilahan sampah berbasis citra memerlukan model deteksi objek yang mampu mempertahankan akurasi ketika kualitas visual berubah. Penelitian ini menganalisis ketahanan visual YOLO11n, YOLO11s, dan RT-DETR-l pada enam kelas objek sampah, yaitu biodegradable, cardboard, glass, metal, paper, dan plastic. Data eksperimen terdiri atas 7.324 citra latih, 2.098 citra validasi, dan 1.042 citra uji dengan anotasi bounding box berformat YOLO. Evaluasi dilakukan pada test set normal dan empat skenario gangguan, yaitu pencahayaan redup 50%, pencahayaan terang 50%, kontras rendah, dan Gaussian noise. Metrik evaluasi meliputi precision, recall, F1-score, mAP@50, mAP@50:95, dan estimasi FPS. Hasil pengujian normal menunjukkan bahwa RT-DETR-l memperoleh performa tertinggi dengan precision 0,5329, mAP@50 0,4484, dan mAP@50:95 0,3576. Pada evaluasi robustness, RT-DETR-l tetap paling stabil, khususnya pada skenario redup 50% dengan mAP@50 0,4463. Sebaliknya, YOLO11n menghasilkan efisiensi inferensi tertinggi dengan 178,32 FPS pada kondisi normal. Temuan ini menegaskan adanya trade-off antara akurasi deteksi, ketahanan visual, dan kecepatan inferensi untuk sistem deteksi sampah real-time.
Co-Authors Inggrid Ester Erlinda Simarmata Juliana Rotua Pasaribu Juni Ismail Pangidoan Adventus Ambarita Randi Sumitro Renovand Mikael Situmorang